Car-pool vehicles and other vehicles carrying multiple passengers reduce fuel consumption, pollution, and highway congestion, relative to single-occupancy vehicles. Highway authorities provide various incentives for high occupancy vehicles which include allowing such vehicles to travel in traffic lanes limited to high occupancy vehicles (HOV lanes) and traffic lanes where a toll charged is reduced or eliminated for high occupancy vehicles (HOT lanes). Monetary penalties are imposed on drivers of vehicles travelling with less than a predetermined number of occupants (e.g., less than 2) in these restricted lanes. Recent efforts have been directed toward sensing and image capture systems and methods to effectuate HOV lane enforcement. Further development in this art is needed as entirely automatic solutions for determining the number of occupants in a moving motor vehicle can be quite challenging. Semi-automatic methods that combine machine detection with human verification/inspection are valuable as such methods reduce the workload of human inspectors (law-enforcement officers) and increase the detection rate compared to methods involving entirely human inspection and detection. In the semi-automatic method, an alert signal together with images/video of the motor vehicle is sent to the law-enforcement officer, if an HOV lane violation is detected by the machine. The officer may verify the captured image and decide if further actions are necessary. Such methods increase the productivity of the traffic enforcement authorities.
Manual enforcement of HOV/HOT lanes by law enforcement officers can be difficult and potentially hazardous. Pulling violating motorists over to issue tickets tends to disrupt traffic and can become a safety hazard for both the officer and the vehicle's occupant. Consequently, automated occupancy detection (i.e., the ability to automatically detect human occupants of vehicles), preferably coupled with automated vehicle recognition and ticket mailing, is desirable.
While ordinary visible-light can be used for automated vehicle occupancy detection through the front windshield under ideal conditions, there are shortcomings in real-life traffic conditions. For example, cabin penetration using visible light is easily compromised by factors such as tinted side windshields as well as environmental conditions such as rain, snow, dirt, and the like. Moreover, artificial visible illumination at night may be distracting to drivers. Near infrared illumination has several advantages over visible light illumination including being unobservable by drivers. In the near infrared illumination band at wavelengths between 1.4 um and 2.8 um, human skin, whether light or dark, has reflectance values that are below that of other materials commonly found inside the passenger compartment of a motor vehicle, such as cotton, wool, polyamide, or leather. Such reflectances are shown in FIGS. 12A and 12B. Note that at wavelengths between 1.4 um and 2.8 um, both dark skin and light skin have reflectance values that are similar and that are relatively low values. At wavelengths less than 1.4 um, skin reflectance between dark skin and light skin, starts to diverge to values that are both relatively high and relatively lower, while the reflectance of common upholstery materials found inside the vehicle remains high. Hence the ability to specifically detect humans will improve by working in these critical wavelength bands. Not only does this make such a system more resistant to efforts to defeat it, but the task of human occupancy detection becomes more achievable and more reliable.
Accordingly, what is needed in this art is a system and method for vehicle occupancy detection which is accurate and robust to reflectance noise.